---
title: "Dashboard Coronvirus Indonesia Dan Asean"
author: "Ahmad Sopian"
date: "27/3/2020"
output:
    flexdashboard::flex_dashboard:
      orientation: rows
      source_code: embed
      verical_layout: fill
---

```{r setup, include=FALSE}
#-------------package----------
library(flexdashboard)
#install.packages("devtools")
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data("coronavirus")
update_datasets()
View(coronavirus)
max(coronavirus$date)


`%>%` <- magrittr::`%>%`
#------------------parameter-------------
#set color
#https ://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "red"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "purple"
#-------------------data------------
df <- coronavirus %>%
  #dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Indonesia") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  #dplyr::mutate*unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death),0,death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia",country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>% 
  dplyr::filter(Country.Region == "Indonesia") %>% 
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed), 
    death_cum = cumsum(death),
    #recovered_cum = cumsum(recovered),
    active_cum = (cumsum(active))
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```


Ringkasan
==================================
Row {data-width=400}
----------------------------------
### confirmed {.value-box}
  
```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Jumlah Kasus Postif Covid-19",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```















### death {.value-box}
```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Kasus Kematian Covid-19 (Persentase Kematian)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```

Row
-------------------------------------------------------------------
### **Kumulatif Kasus Positif Covid-19 Di Indonesia** 

```{r}
plotly::plot_ly(data=df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+makers",
    # name = "active",
    name = "Positif Covid-19",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Kematian",
    line = list(color = death_color),
    mmarker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-02"),
    y = 1,
    text = paste("Kasus Pertama",
                 "
",
                 "Positif Covid-19"),
    xref = "x",
    yref = "y",
    arrowhead = 3,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -100,
    ay = -120
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-11"),
    y = 3,
    text = paste("Kematian Pertama",
                 "
",
                 "Pasien Positif Covid-19"),
    xref = "x",
    yref = "y",
    arrowhead = 3,
    arrowhead = 3,
    arrowsize = 1,
    arrowrow = TRUE,
    ax = -40,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-26"),
    y = 893,
    text = paste("Positif Covid-19",
                 "
",
                 "Tanggal (26/03)"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Angka Kumulatif Kasus Covid-19"),
    xaxis = list(title = "Tanggal"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Perbandingan
===============================================================

Column {data-width=400}
------------------------------

### **Tambahan Kasus Per-Hari Positif Covid-19 Asean**

```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-03-02") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#-----------------------------------------
# plotting data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Indonesia,
    type = "scatter",
    mode = "lines+markers",
    name = "Indonesia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Malaysia,
    type = "scatter",
    mode = "lines+markers",
    name = "Malaysia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Singapore,
    type = "scatter",
    mode = "lines+markers",
    name = "Singapore"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Philippines,
    type = "scatter",
    mode = "lines+markers",
    name = "Filipina"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Thailand,
    type = "scatter",
    mode = "lines+markers",
    name = "Thailand"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Brunei,
    type = "scatter",
    mode = "lines+markers",
    name = "Brunei Darusallam"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Vietnam,
    type = "scatter",
    mode = "lines+markers",
    name = "Vietnam"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Laos,
    type = "scatter",
    mode = "lines+markers",
    name = "Laos"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Cambodia,
    type = "scatter",
    mode = "lines+markers",
    name = "Kamboja"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Angka Kasus Positif Covid-19 (ASEAN)"),
    xaxis = list(title = "Tanggal"),
    #paper_bgcolor = "black",
    #plot_bgcolor = "black",
    #font = list(color = "white"),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Kumulatif Jumlah Kasus Positif Covid-19 ASEAN**

```{r daily_summary}
df_Asean <- coronavirus %>%
  # dplyr::filter(Country.Region == max(date)) %>%
  dplyr::filter(Country.Region == "Indonesia" |
                  Country.Region == "Malaysia" |
                  Country.Region == "Singapore" |
                  Country.Region == "Philippines" |
                  Country.Region == "Thailand" |
                  Country.Region == "Brunei" |
                  Country.Region == "Vietnam" | 
                  Country.Region == "Laos" |
                  Country.Region == "Cambodia") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_Asean,
  x = ~country,
  # y = ~unrecovered,
  y = ~confirmed,
  # text = ~confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Positif Covid-19",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text = ~death,
    # textposition = 'auto',
    name = "Kematian Karena Covid-19",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Angka Kumulatif Positif Covid-19"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

Peta
==================================================

### **Peta ASEAN Kasus Covid-19** (*Gunakan (+) atau (-) icons untuk zoom in/out*)

```{r}
library(leaflet)
library(leafpop)
library(purrr)
cv_data_plot_asean <- coronavirus %>%
  dplyr::filter(Country.Region == "Indonesia" |
                  Country.Region == "Malaysia" |
                  Country.Region == "Singapore" |
                  Country.Region == "Philippines" |
                  Country.Region == "Thailand" |
                  Country.Region == "Brunei" |
                  Country.Region == "Vietnam" | 
                  Country.Region == "Laos" |
                  Country.Region == "Cambodia") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_plot_asean.split <- cv_data_plot_asean %>% split(cv_data_plot_asean$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed","death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_plot_asean.split) %>%
  purrr::walk(function(df){
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_plot_asean.split[[df]],
        lng = ~Long, lat = ~Lat,
        # label=~as.character(cases),
        color = ~pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_plot_asean.split[[df]],
                                    feature.id = FALSE,
                                    row.numbers = FALSE,
                                    zcol = c("type","cases","Country.Region", "Province.State")
                                    ),
        group = df,
        # clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_plot_asean.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```